Overview

Brought to you by YData

Dataset statistics

Number of variables40
Number of observations11789
Missing cells11789
Missing cells (%)2.5%
Total size in memory3.6 MiB
Average record size in memory320.0 B

Variable types

Text9
Numeric30
Unsupported1

Alerts

workload_type has 11789 (100.0%) missing valuesMissing
data_units_written has unique valuesUnique
host_read_commands has unique valuesUnique
host_write_commands has unique valuesUnique
host_read_cmds_per_power_cycle has unique valuesUnique
workload_type is an unsupported type, check if it needs cleaning or further analysisUnsupported
unsafe_shutdowns has 2938 (24.9%) zerosZeros
media_errors has 2879 (24.4%) zerosZeros
error_information_log_entries has 2879 (24.4%) zerosZeros
bad_block_count_grown has 1932 (16.4%) zerosZeros
pcie_correctable_errors has 217 (1.8%) zerosZeros
pcie_uncorrectable_errors has 3837 (32.5%) zerosZeros

Reproduction

Analysis started2025-12-16 03:55:18.967234
Analysis finished2025-12-16 03:55:19.568856
Duration0.6 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct11784
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:19.954193image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters294725
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11779 ?
Unique (%)99.9%

Sample

1st row2025-03-06 10:57:50+00:00
2nd row2025-07-11 06:02:07+00:00
3rd row2025-07-30 15:49:04+00:00
4th row2025-05-24 10:06:20+00:00
5th row2025-07-04 16:23:54+00:00
ValueCountFrequency (%)
2025-05-2491
 
0.4%
2025-06-0789
 
0.4%
2025-06-2887
 
0.4%
2025-04-1385
 
0.4%
2025-05-2685
 
0.4%
2025-02-1684
 
0.4%
2025-07-3084
 
0.4%
2025-04-3084
 
0.4%
2025-02-1183
 
0.4%
2025-04-0283
 
0.4%
Other values (11186)22723
96.4%
2025-12-15T20:55:20.588735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
087590
29.7%
239807
13.5%
:35367
12.0%
-23578
 
8.0%
522284
 
7.6%
117951
 
6.1%
11789
 
4.0%
+11789
 
4.0%
311697
 
4.0%
410702
 
3.6%
Other values (4)22171
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)294725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
087590
29.7%
239807
13.5%
:35367
12.0%
-23578
 
8.0%
522284
 
7.6%
117951
 
6.1%
11789
 
4.0%
+11789
 
4.0%
311697
 
4.0%
410702
 
3.6%
Other values (4)22171
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)294725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
087590
29.7%
239807
13.5%
:35367
12.0%
-23578
 
8.0%
522284
 
7.6%
117951
 
6.1%
11789
 
4.0%
+11789
 
4.0%
311697
 
4.0%
410702
 
3.6%
Other values (4)22171
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)294725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
087590
29.7%
239807
13.5%
:35367
12.0%
-23578
 
8.0%
522284
 
7.6%
117951
 
6.1%
11789
 
4.0%
+11789
 
4.0%
311697
 
4.0%
410702
 
3.6%
Other values (4)22171
 
7.5%

ff
Text

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:20.816484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.40003393
Min length3

Characters and Unicode

Total characters40083
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE1.s
2nd rowE1.s
3rd rowE1.s
4th rowE1.s
5th rowE1.s
ValueCountFrequency (%)
e1.s2358
20.0%
e3.s2358
20.0%
u.22358
20.0%
u.32358
20.0%
m.22357
20.0%
2025-12-15T20:55:21.248681image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.11789
29.4%
E4716
11.8%
s4716
11.8%
34716
11.8%
U4716
11.8%
24715
 
11.8%
12358
 
5.9%
M2357
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)40083
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.11789
29.4%
E4716
11.8%
s4716
11.8%
34716
11.8%
U4716
11.8%
24715
 
11.8%
12358
 
5.9%
M2357
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)40083
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.11789
29.4%
E4716
11.8%
s4716
11.8%
34716
11.8%
U4716
11.8%
24715
 
11.8%
12358
 
5.9%
M2357
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)40083
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.11789
29.4%
E4716
11.8%
s4716
11.8%
34716
11.8%
U4716
11.8%
24715
 
11.8%
12358
 
5.9%
M2357
 
5.9%
Distinct10227
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:21.702860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters70734
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8824 ?
Unique (%)74.8%

Sample

1st rowPM6031
2nd rowMT3959
3rd rowMT4566
4th rowNV7356
5th rowMT2426
ValueCountFrequency (%)
nv63435
 
< 0.1%
pm24995
 
< 0.1%
xg46074
 
< 0.1%
pm55154
 
< 0.1%
nv81574
 
< 0.1%
nv03864
 
< 0.1%
nv04794
 
< 0.1%
nv14594
 
< 0.1%
nv15934
 
< 0.1%
mt93324
 
< 0.1%
Other values (10217)11747
99.6%
2025-12-15T20:55:22.338051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M5882
 
8.3%
44857
 
6.9%
24776
 
6.8%
64750
 
6.7%
14739
 
6.7%
54731
 
6.7%
34718
 
6.7%
94691
 
6.6%
84665
 
6.6%
74620
 
6.5%
Other values (7)22305
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)70734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M5882
 
8.3%
44857
 
6.9%
24776
 
6.8%
64750
 
6.7%
14739
 
6.7%
54731
 
6.7%
34718
 
6.7%
94691
 
6.6%
84665
 
6.6%
74620
 
6.5%
Other values (7)22305
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)70734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M5882
 
8.3%
44857
 
6.9%
24776
 
6.8%
64750
 
6.7%
14739
 
6.7%
54731
 
6.7%
34718
 
6.7%
94691
 
6.6%
84665
 
6.6%
74620
 
6.5%
Other values (7)22305
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)70734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M5882
 
8.3%
44857
 
6.9%
24776
 
6.8%
64750
 
6.7%
14739
 
6.7%
54731
 
6.7%
34718
 
6.7%
94691
 
6.6%
84665
 
6.6%
74620
 
6.5%
Other values (7)22305
31.5%
Distinct11696
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:22.720184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters94312
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11604 ?
Unique (%)98.4%

Sample

1st rowF63939.1
2nd rowF57681.7
3rd rowF38619.0
4th rowF70998.7
5th rowF78133.8
ValueCountFrequency (%)
f96570.83
 
< 0.1%
f99295.82
 
< 0.1%
f60276.12
 
< 0.1%
f93371.72
 
< 0.1%
f82618.82
 
< 0.1%
f33220.82
 
< 0.1%
f71655.62
 
< 0.1%
f31458.82
 
< 0.1%
f93190.42
 
< 0.1%
f55514.22
 
< 0.1%
Other values (11686)11768
99.8%
2025-12-15T20:55:23.321031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F11789
12.5%
.11789
12.5%
27325
7.8%
77321
7.8%
67281
7.7%
87220
7.7%
97185
7.6%
17164
7.6%
57139
7.6%
37100
7.5%
Other values (2)12999
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)94312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F11789
12.5%
.11789
12.5%
27325
7.8%
77321
7.8%
67281
7.7%
87220
7.7%
97185
7.6%
17164
7.6%
57139
7.6%
37100
7.5%
Other values (2)12999
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)94312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F11789
12.5%
.11789
12.5%
27325
7.8%
77321
7.8%
67281
7.7%
87220
7.7%
97185
7.6%
17164
7.6%
57139
7.6%
37100
7.5%
Other values (2)12999
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)94312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F11789
12.5%
.11789
12.5%
27325
7.8%
77321
7.8%
67281
7.7%
87220
7.7%
97185
7.6%
17164
7.6%
57139
7.6%
37100
7.5%
Other values (2)12999
13.8%

nvme_capacity_tb
Real number (ℝ)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.88896429
Minimum4
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:23.517538image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14
median8
Q315
95-th percentile60
Maximum60
Range56
Interquartile range (IQR)11

Descriptive statistics

Standard deviation16.9290212
Coefficient of variation (CV)1.065457817
Kurtosis2.225426141
Mean15.88896429
Median Absolute Deviation (MAD)4
Skewness1.8456097
Sum187315
Variance286.5917589
MonotonicityNot monotonic
2025-12-15T20:55:23.702389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
43930
33.3%
82620
22.2%
152619
22.2%
251310
 
11.1%
601310
 
11.1%
ValueCountFrequency (%)
43930
33.3%
82620
22.2%
152619
22.2%
251310
 
11.1%
601310
 
11.1%
ValueCountFrequency (%)
601310
 
11.1%
251310
 
11.1%
152619
22.2%
82620
22.2%
43930
33.3%

overprovisioning_ratio
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.028840444
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:23.865119image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q36
95-th percentile7
Maximum8
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.435592518
Coefficient of variation (CV)0.2854718765
Kurtosis-1.24156032
Mean5.028840444
Median Absolute Deviation (MAD)1
Skewness-0.02713597175
Sum59285
Variance2.060925878
MonotonicityNot monotonic
2025-12-15T20:55:24.065592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
72405
20.4%
62392
20.3%
42389
20.3%
52271
19.3%
32217
18.8%
264
 
0.5%
851
 
0.4%
ValueCountFrequency (%)
264
 
0.5%
32217
18.8%
42389
20.3%
52271
19.3%
62392
20.3%
ValueCountFrequency (%)
851
 
0.4%
72405
20.4%
62392
20.3%
52271
19.3%
42389
20.3%

composite_temperature_c
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.94248876
Minimum28
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:24.250450image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile28
Q131
median34
Q337
95-th percentile40
Maximum40
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.746087286
Coefficient of variation (CV)0.1103657222
Kurtosis-1.215561971
Mean33.94248876
Median Absolute Deviation (MAD)3
Skewness0.02188773646
Sum400148
Variance14.03316995
MonotonicityNot monotonic
2025-12-15T20:55:24.450928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
31941
 
8.0%
29934
 
7.9%
30932
 
7.9%
28921
 
7.8%
37920
 
7.8%
40910
 
7.7%
34909
 
7.7%
36906
 
7.7%
33904
 
7.7%
35898
 
7.6%
Other values (3)2614
22.2%
ValueCountFrequency (%)
28921
7.8%
29934
7.9%
30932
7.9%
31941
8.0%
32881
7.5%
ValueCountFrequency (%)
40910
7.7%
39873
7.4%
38860
7.3%
37920
7.8%
36906
7.7%

data_units_read
Real number (ℝ)

Distinct11788
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104816479.7
Minimum10010381
Maximum199991493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:24.667027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10010381
5-th percentile19325422.6
Q157884263
median104671926
Q3152470509
95-th percentile190883342
Maximum199991493
Range189981112
Interquartile range (IQR)94586246

Descriptive statistics

Standard deviation54843331.4
Coefficient of variation (CV)0.5232319534
Kurtosis-1.196565337
Mean104816479.7
Median Absolute Deviation (MAD)47235936
Skewness0.01436677161
Sum1.235681479 × 1012
Variance3.007790999 × 1015
MonotonicityNot monotonic
2025-12-15T20:55:24.905253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
891660702
 
< 0.1%
1939837131
 
< 0.1%
1847094911
 
< 0.1%
913156491
 
< 0.1%
1347931441
 
< 0.1%
1730792341
 
< 0.1%
432630441
 
< 0.1%
677127941
 
< 0.1%
986165051
 
< 0.1%
242534311
 
< 0.1%
Other values (11778)11778
99.9%
ValueCountFrequency (%)
100103811
< 0.1%
100169461
< 0.1%
100208711
< 0.1%
100520561
< 0.1%
100813711
< 0.1%
ValueCountFrequency (%)
1999914931
< 0.1%
1999744191
< 0.1%
1999584971
< 0.1%
1999475311
< 0.1%
1999439831
< 0.1%

data_units_written
Real number (ℝ)

Unique 

Distinct11789
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79008868.85
Minimum8014004
Maximum149991035
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:25.137043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum8014004
5-th percentile15286472.8
Q143942714
median78411310
Q3114463978
95-th percentile143197421.6
Maximum149991035
Range141977031
Interquartile range (IQR)70521264

Descriptive statistics

Standard deviation40921700.95
Coefficient of variation (CV)0.5179380689
Kurtosis-1.196343113
Mean79008868.85
Median Absolute Deviation (MAD)35321403
Skewness0.01210414709
Sum9.314355548 × 1011
Variance1.674585609 × 1015
MonotonicityNot monotonic
2025-12-15T20:55:25.368763image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
465507751
 
< 0.1%
132888331
 
< 0.1%
1096334641
 
< 0.1%
423511141
 
< 0.1%
557179471
 
< 0.1%
774479651
 
< 0.1%
1434381831
 
< 0.1%
685347771
 
< 0.1%
723025031
 
< 0.1%
159145231
 
< 0.1%
Other values (11779)11779
99.9%
ValueCountFrequency (%)
80140041
< 0.1%
80190551
< 0.1%
80436331
< 0.1%
80493231
< 0.1%
80746281
< 0.1%
ValueCountFrequency (%)
1499910351
< 0.1%
1499825711
< 0.1%
1499718811
< 0.1%
1499713201
< 0.1%
1499649951
< 0.1%

host_read_commands
Real number (ℝ)

Unique 

Distinct11789
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1243670591
Minimum500086728
Maximum1999971621
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:25.871114image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum500086728
5-th percentile569837373.2
Q1874099537
median1239987541
Q31618012883
95-th percentile1922547820
Maximum1999971621
Range1499884893
Interquartile range (IQR)743913346

Descriptive statistics

Standard deviation430141167.3
Coefficient of variation (CV)0.3458642268
Kurtosis-1.181331035
Mean1243670591
Median Absolute Deviation (MAD)372071931
Skewness0.01719453985
Sum1.466163259 × 1013
Variance1.850214238 × 1017
MonotonicityNot monotonic
2025-12-15T20:55:26.119894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17387717361
 
< 0.1%
15015901381
 
< 0.1%
10790528541
 
< 0.1%
15994142181
 
< 0.1%
16532946341
 
< 0.1%
19785944321
 
< 0.1%
17094923671
 
< 0.1%
5776884291
 
< 0.1%
9612282951
 
< 0.1%
16155779491
 
< 0.1%
Other values (11779)11779
99.9%
ValueCountFrequency (%)
5000867281
< 0.1%
5000999941
< 0.1%
5003005901
< 0.1%
5003341221
< 0.1%
5004923341
< 0.1%
ValueCountFrequency (%)
19999716211
< 0.1%
19999150821
< 0.1%
19998518121
< 0.1%
19997074681
< 0.1%
19992473021
< 0.1%

host_write_commands
Real number (ℝ)

Unique 

Distinct11789
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean931329999.3
Minimum307668421
Maximum1786929759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:26.335996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum307668421
5-th percentile423414030.4
Q1649002060
median918451301
Q31189463713
95-th percentile1515219082
Maximum1786929759
Range1479261338
Interquartile range (IQR)540461653

Descriptive statistics

Standard deviation340055393.9
Coefficient of variation (CV)0.365128788
Kurtosis-0.8656495435
Mean931329999.3
Median Absolute Deviation (MAD)270030955
Skewness0.2060099243
Sum1.097944936 × 1013
Variance1.156376709 × 1017
MonotonicityNot monotonic
2025-12-15T20:55:26.583339image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13413530001
 
< 0.1%
10824160021
 
< 0.1%
8094223911
 
< 0.1%
13015867871
 
< 0.1%
10272052711
 
< 0.1%
15084109091
 
< 0.1%
12054923641
 
< 0.1%
3534874171
 
< 0.1%
8031778211
 
< 0.1%
11001617431
 
< 0.1%
Other values (11779)11779
99.9%
ValueCountFrequency (%)
3076684211
< 0.1%
3089733631
< 0.1%
3090018211
< 0.1%
3102509121
< 0.1%
3132777631
< 0.1%
ValueCountFrequency (%)
17869297591
< 0.1%
17764977671
< 0.1%
17748455331
< 0.1%
17736600091
< 0.1%
17716382501
< 0.1%

avg_queue_depth
Real number (ℝ)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.06183731
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:26.768201image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q316
95-th percentile32
Maximum32
Range31
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.00501135
Coefficient of variation (CV)0.9123826723
Kurtosis-0.6392986085
Mean12.06183731
Median Absolute Deviation (MAD)7
Skewness0.8736586576
Sum142197
Variance121.1102749
MonotonicityNot monotonic
2025-12-15T20:55:26.953055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
82412
20.5%
42408
20.4%
162354
20.0%
12325
19.7%
322290
19.4%
ValueCountFrequency (%)
12325
19.7%
42408
20.4%
82412
20.5%
162354
20.0%
322290
19.4%
ValueCountFrequency (%)
322290
19.4%
162354
20.0%
82412
20.5%
42408
20.4%
12325
19.7%

iops
Real number (ℝ)

Distinct11177
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64639.67309
Minimum10006
Maximum119982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:27.169156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10006
5-th percentile15333.6
Q137200
median64445
Q392364
95-th percentile114016.4
Maximum119982
Range109976
Interquartile range (IQR)55164

Descriptive statistics

Standard deviation31854.6637
Coefficient of variation (CV)0.4928036015
Kurtosis-1.214442704
Mean64639.67309
Median Absolute Deviation (MAD)27572
Skewness0.01619518328
Sum762037106
Variance1014719599
MonotonicityNot monotonic
2025-12-15T20:55:27.400873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127053
 
< 0.1%
771753
 
< 0.1%
646323
 
< 0.1%
210663
 
< 0.1%
1115253
 
< 0.1%
932933
 
< 0.1%
514493
 
< 0.1%
122063
 
< 0.1%
150723
 
< 0.1%
1008313
 
< 0.1%
Other values (11167)11759
99.7%
ValueCountFrequency (%)
100061
< 0.1%
100101
< 0.1%
100151
< 0.1%
100321
< 0.1%
100351
< 0.1%
ValueCountFrequency (%)
1199821
< 0.1%
1199771
< 0.1%
1199631
< 0.1%
1199621
< 0.1%
1199551
< 0.1%

bandwidth_read_gbps
Real number (ℝ)

Distinct51
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.510323183
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:27.623475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.3
Q12.3
median3.5
Q34.8
95-th percentile5.7
Maximum6
Range5
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.436704182
Coefficient of variation (CV)0.4092797464
Kurtosis-1.191978852
Mean3.510323183
Median Absolute Deviation (MAD)1.2
Skewness-0.007751432071
Sum41383.2
Variance2.064118906
MonotonicityNot monotonic
2025-12-15T20:55:27.870820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.2265
 
2.2%
2258
 
2.2%
2.6257
 
2.2%
3249
 
2.1%
3.1249
 
2.1%
4.9247
 
2.1%
4.5247
 
2.1%
3.2247
 
2.1%
1.6246
 
2.1%
4.3245
 
2.1%
Other values (41)9279
78.7%
ValueCountFrequency (%)
1119
1.0%
1.1227
1.9%
1.2227
1.9%
1.3212
1.8%
1.4217
1.8%
ValueCountFrequency (%)
6112
1.0%
5.9222
1.9%
5.8241
2.0%
5.7236
2.0%
5.6242
2.1%

bandwidth_write_gbps
Real number (ℝ)

Distinct41
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.993527865
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:28.086920image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2
Q12
median3
Q34
95-th percentile4.8
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.158783316
Coefficient of variation (CV)0.3870962183
Kurtosis-1.202385437
Mean2.993527865
Median Absolute Deviation (MAD)1
Skewness0.005669272337
Sum35290.7
Variance1.342778773
MonotonicityNot monotonic
2025-12-15T20:55:28.325148image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1.3326
 
2.8%
3.4321
 
2.7%
2.6319
 
2.7%
3.2318
 
2.7%
4.5318
 
2.7%
2.8317
 
2.7%
4.4315
 
2.7%
3.3314
 
2.7%
2313
 
2.7%
1.2306
 
2.6%
Other values (31)8622
73.1%
ValueCountFrequency (%)
1143
1.2%
1.1288
2.4%
1.2306
2.6%
1.3326
2.8%
1.4293
2.5%
ValueCountFrequency (%)
5150
1.3%
4.9299
2.5%
4.8302
2.6%
4.7273
2.3%
4.6278
2.4%

io_completion_time_ms
Real number (ℝ)

Distinct451
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2754694206
Minimum0.05
Maximum0.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:28.541253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.073
Q10.163
median0.275
Q30.388
95-th percentile0.479
Maximum0.5
Range0.45
Interquartile range (IQR)0.225

Descriptive statistics

Standard deviation0.130052147
Coefficient of variation (CV)0.472111012
Kurtosis-1.197568892
Mean0.2754694206
Median Absolute Deviation (MAD)0.113
Skewness0.0009748319315
Sum3247.509
Variance0.01691356093
MonotonicityNot monotonic
2025-12-15T20:55:28.788596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15843
 
0.4%
0.16543
 
0.4%
0.14341
 
0.3%
0.34141
 
0.3%
0.4840
 
0.3%
0.39539
 
0.3%
0.38639
 
0.3%
0.34939
 
0.3%
0.18938
 
0.3%
0.36638
 
0.3%
Other values (441)11388
96.6%
ValueCountFrequency (%)
0.0510
 
0.1%
0.05124
0.2%
0.05229
0.2%
0.05316
0.1%
0.05434
0.3%
ValueCountFrequency (%)
0.516
0.1%
0.49924
0.2%
0.49827
0.2%
0.49729
0.2%
0.49626
0.2%

power_cycles
Real number (ℝ)

Distinct50
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.35931801
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:28.989100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median25
Q338
95-th percentile48
Maximum50
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.44134413
Coefficient of variation (CV)0.5694689471
Kurtosis-1.201080977
Mean25.35931801
Median Absolute Deviation (MAD)13
Skewness0.007205767665
Sum298961
Variance208.5524202
MonotonicityNot monotonic
2025-12-15T20:55:29.227364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19265
 
2.2%
30262
 
2.2%
21261
 
2.2%
15258
 
2.2%
26256
 
2.2%
39256
 
2.2%
41253
 
2.1%
2253
 
2.1%
48252
 
2.1%
25252
 
2.1%
Other values (40)9221
78.2%
ValueCountFrequency (%)
1243
2.1%
2253
2.1%
3240
2.0%
4245
2.1%
5238
2.0%
ValueCountFrequency (%)
50231
2.0%
49210
1.8%
48252
2.1%
47240
2.0%
46235
2.0%

power_on_hours
Real number (ℝ)

Distinct5722
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4488.10162
Minimum1000
Maximum8000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:29.443467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1334.8
Q12753
median4499
Q36222
95-th percentile7654
Maximum8000
Range7000
Interquartile range (IQR)3469

Descriptive statistics

Standard deviation2019.039586
Coefficient of variation (CV)0.4498649444
Kurtosis-1.18439775
Mean4488.10162
Median Absolute Deviation (MAD)1733
Skewness-0.0003944985841
Sum52910230
Variance4076520.848
MonotonicityNot monotonic
2025-12-15T20:55:29.690808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63998
 
0.1%
77808
 
0.1%
55828
 
0.1%
67767
 
0.1%
13857
 
0.1%
40267
 
0.1%
39847
 
0.1%
41227
 
0.1%
50567
 
0.1%
43097
 
0.1%
Other values (5712)11716
99.4%
ValueCountFrequency (%)
10001
 
< 0.1%
10013
< 0.1%
10022
< 0.1%
10032
< 0.1%
10042
< 0.1%
ValueCountFrequency (%)
80001
 
< 0.1%
79991
 
< 0.1%
79973
< 0.1%
79962
< 0.1%
79952
< 0.1%

controller_busy_time
Real number (ℝ)

Distinct901
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean550.4703537
Minimum100
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:29.906905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile145
Q1325
median549
Q3775
95-th percentile955
Maximum1000
Range900
Interquartile range (IQR)450

Descriptive statistics

Standard deviation259.3019572
Coefficient of variation (CV)0.4710552629
Kurtosis-1.193524659
Mean550.4703537
Median Absolute Deviation (MAD)225
Skewness-0.002112404421
Sum6489495
Variance67237.505
MonotonicityNot monotonic
2025-12-15T20:55:30.145146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61128
 
0.2%
47626
 
0.2%
85425
 
0.2%
18125
 
0.2%
27725
 
0.2%
32924
 
0.2%
73624
 
0.2%
49223
 
0.2%
59023
 
0.2%
83923
 
0.2%
Other values (891)11543
97.9%
ValueCountFrequency (%)
1009
0.1%
10113
0.1%
10216
0.1%
10314
0.1%
10419
0.2%
ValueCountFrequency (%)
100014
0.1%
99912
0.1%
9989
0.1%
99715
0.1%
99621
0.2%

percentage_used
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.476885232
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:30.503008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.865272322
Coefficient of variation (CV)0.5231572692
Kurtosis-1.211406715
Mean5.476885232
Median Absolute Deviation (MAD)2
Skewness0.009374193668
Sum64567
Variance8.209785479
MonotonicityNot monotonic
2025-12-15T20:55:30.684444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
61251
10.6%
51210
10.3%
21208
10.2%
11183
10.0%
71181
10.0%
31176
10.0%
101170
9.9%
91153
9.8%
41129
9.6%
81128
9.6%
ValueCountFrequency (%)
11183
10.0%
21208
10.2%
31176
10.0%
41129
9.6%
51210
10.3%
ValueCountFrequency (%)
101170
9.9%
91153
9.8%
81128
9.6%
71181
10.0%
61251
10.6%

wear_level_avg
Real number (ℝ)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.996522182
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:30.869303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.40702276
Coefficient of variation (CV)0.4695519255
Kurtosis-1.286894122
Mean2.996522182
Median Absolute Deviation (MAD)1
Skewness0.01238152752
Sum35326
Variance1.979713048
MonotonicityNot monotonic
2025-12-15T20:55:31.069782image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
22423
20.6%
32402
20.4%
52342
19.9%
42314
19.6%
12308
19.6%
ValueCountFrequency (%)
12308
19.6%
22423
20.6%
32402
20.4%
42314
19.6%
52342
19.9%
ValueCountFrequency (%)
52342
19.9%
42314
19.6%
32402
20.4%
22423
20.6%
12308
19.6%

wear_level_max
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.496394944
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:31.270262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q36
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.791922285
Coefficient of variation (CV)0.3985242193
Kurtosis-0.7022432001
Mean4.496394944
Median Absolute Deviation (MAD)1
Skewness-0.007568541881
Sum53008
Variance3.210985475
MonotonicityNot monotonic
2025-12-15T20:55:31.455132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
52385
20.2%
42258
19.2%
31853
15.7%
61829
15.5%
21169
9.9%
71169
9.9%
1573
 
4.9%
8553
 
4.7%
ValueCountFrequency (%)
1573
 
4.9%
21169
9.9%
31853
15.7%
42258
19.2%
52385
20.2%
ValueCountFrequency (%)
8553
 
4.7%
71169
9.9%
61829
15.5%
52385
20.2%
42258
19.2%
Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.52311477
Minimum90
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:31.639985image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile90
Q192
median95
Q397
95-th percentile99
Maximum99
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.865272322
Coefficient of variation (CV)0.03031292747
Kurtosis-1.211406715
Mean94.52311477
Median Absolute Deviation (MAD)2
Skewness-0.009374193668
Sum1114333
Variance8.209785479
MonotonicityNot monotonic
2025-12-15T20:55:31.840463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
941251
10.6%
951210
10.3%
981208
10.2%
991183
10.0%
931181
10.0%
971176
10.0%
901170
9.9%
911153
9.8%
961129
9.6%
921128
9.6%
ValueCountFrequency (%)
901170
9.9%
911153
9.8%
921128
9.6%
931181
10.0%
941251
10.6%
ValueCountFrequency (%)
991183
10.0%
981208
10.2%
971176
10.0%
961129
9.6%
951210
10.3%

unsafe_shutdowns
Real number (ℝ)

Zeros 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.497667317
Minimum0
Maximum3
Zeros2938
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:32.003186image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.115458286
Coefficient of variation (CV)0.7447971077
Kurtosis-1.354039114
Mean1.497667317
Median Absolute Deviation (MAD)1
Skewness0.002920115466
Sum17656
Variance1.244247188
MonotonicityNot monotonic
2025-12-15T20:55:32.203665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
12968
25.2%
22961
25.1%
02938
24.9%
32922
24.8%
ValueCountFrequency (%)
02938
24.9%
12968
25.2%
22961
25.1%
32922
24.8%
ValueCountFrequency (%)
32922
24.8%
22961
25.1%
12968
25.2%
02938
24.9%

background_scrub_time_pct
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5509797269
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:32.388521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.3
median0.6
Q30.8
95-th percentile1
Maximum1
Range0.9
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.2610505102
Coefficient of variation (CV)0.473793313
Kurtosis-1.141608494
Mean0.5509797269
Median Absolute Deviation (MAD)0.2
Skewness-0.003239548804
Sum6495.5
Variance0.06814736886
MonotonicityNot monotonic
2025-12-15T20:55:32.589000image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.31370
11.6%
0.81350
11.5%
0.61331
11.3%
0.51322
11.2%
0.71318
11.2%
0.21294
11.0%
0.91292
11.0%
0.41269
10.8%
1632
5.4%
0.1611
5.2%
ValueCountFrequency (%)
0.1611
5.2%
0.21294
11.0%
0.31370
11.6%
0.41269
10.8%
0.51322
11.2%
ValueCountFrequency (%)
1632
5.4%
0.91292
11.0%
0.81350
11.5%
0.71318
11.2%
0.61331
11.3%

gc_active_time_pct
Real number (ℝ)

Distinct51
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.488548647
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:32.805099image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2
Q12.2
median3.5
Q34.7
95-th percentile5.8
Maximum6
Range5
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.443149399
Coefficient of variation (CV)0.4136818905
Kurtosis-1.18650416
Mean3.488548647
Median Absolute Deviation (MAD)1.2
Skewness0.01910776275
Sum41126.5
Variance2.082680189
MonotonicityNot monotonic
2025-12-15T20:55:33.043329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.2277
 
2.3%
1.2272
 
2.3%
4.5264
 
2.2%
3.7263
 
2.2%
2.7262
 
2.2%
2.2261
 
2.2%
2.5258
 
2.2%
2.6255
 
2.2%
4.7253
 
2.1%
1.8247
 
2.1%
Other values (41)9177
77.8%
ValueCountFrequency (%)
1109
0.9%
1.1226
1.9%
1.2272
2.3%
1.3240
2.0%
1.4235
2.0%
ValueCountFrequency (%)
6143
1.2%
5.9245
2.1%
5.8225
1.9%
5.7236
2.0%
5.6233
2.0%

media_errors
Real number (ℝ)

Zeros 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.505301552
Minimum0
Maximum3
Zeros2879
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:33.221674image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.109194333
Coefficient of variation (CV)0.736858559
Kurtosis-1.339465303
Mean1.505301552
Median Absolute Deviation (MAD)1
Skewness-0.009639541033
Sum17746
Variance1.230312068
MonotonicityNot monotonic
2025-12-15T20:55:33.406541image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
23038
25.8%
12973
25.2%
32899
24.6%
02879
24.4%
ValueCountFrequency (%)
02879
24.4%
12973
25.2%
23038
25.8%
32899
24.6%
ValueCountFrequency (%)
32899
24.6%
23038
25.8%
12973
25.2%
02879
24.4%

error_information_log_entries
Real number (ℝ)

Zeros 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.931885656
Minimum0
Maximum20
Zeros2879
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:33.607018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q314
95-th percentile19
Maximum20
Range20
Interquartile range (IQR)13

Descriptive statistics

Standard deviation6.728446871
Coefficient of variation (CV)0.8482783493
Kurtosis-1.308074134
Mean7.931885656
Median Absolute Deviation (MAD)7
Skewness0.2718447602
Sum93509
Variance45.2719973
MonotonicityNot monotonic
2025-12-15T20:55:33.829627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
02879
24.4%
11483
 
4.1%
6478
 
4.1%
8471
 
4.0%
14469
 
4.0%
15462
 
3.9%
2458
 
3.9%
18457
 
3.9%
10454
 
3.9%
7452
 
3.8%
Other values (11)4726
40.1%
ValueCountFrequency (%)
02879
24.4%
1442
 
3.7%
2458
 
3.9%
3408
 
3.5%
4452
 
3.8%
ValueCountFrequency (%)
20432
3.7%
19443
3.8%
18457
3.9%
17426
3.6%
16446
3.8%

bad_block_count_grown
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.513529561
Minimum0
Maximum5
Zeros1932
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:34.030105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.699345471
Coefficient of variation (CV)0.6760793653
Kurtosis-1.252328611
Mean2.513529561
Median Absolute Deviation (MAD)1
Skewness-0.0119962688
Sum29632
Variance2.887775028
MonotonicityNot monotonic
2025-12-15T20:55:34.224076image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
22053
17.4%
41990
16.9%
31964
16.7%
51956
16.6%
01932
16.4%
11894
16.1%
ValueCountFrequency (%)
01932
16.4%
11894
16.1%
22053
17.4%
31964
16.7%
41990
16.9%
ValueCountFrequency (%)
51956
16.6%
41990
16.9%
31964
16.7%
22053
17.4%
11894
16.1%

pcie_correctable_errors
Real number (ℝ)

Zeros 

Distinct51
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.17227924
Minimum0
Maximum50
Zeros217
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:34.431061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q113
median25
Q338
95-th percentile48
Maximum50
Range50
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.63830725
Coefficient of variation (CV)0.5815249031
Kurtosis-1.189563323
Mean25.17227924
Median Absolute Deviation (MAD)13
Skewness-0.01263608417
Sum296756
Variance214.2800391
MonotonicityNot monotonic
2025-12-15T20:55:34.662786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6271
 
2.3%
32267
 
2.3%
27262
 
2.2%
43252
 
2.1%
4251
 
2.1%
25249
 
2.1%
46249
 
2.1%
26245
 
2.1%
39243
 
2.1%
9242
 
2.1%
Other values (41)9258
78.5%
ValueCountFrequency (%)
0217
1.8%
1201
1.7%
2201
1.7%
3237
2.0%
4251
2.1%
ValueCountFrequency (%)
50231
2.0%
49232
2.0%
48239
2.0%
47213
1.8%
46249
2.1%

pcie_uncorrectable_errors
Real number (ℝ)

Zeros 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.010772754
Minimum0
Maximum2
Zeros3837
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:34.847641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.8134240125
Coefficient of variation (CV)0.804754589
Kurtosis-1.488383518
Mean1.010772754
Median Absolute Deviation (MAD)1
Skewness-0.01971917917
Sum11916
Variance0.661658624
MonotonicityNot monotonic
2025-12-15T20:55:35.048115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
13988
33.8%
23964
33.6%
03837
32.5%
ValueCountFrequency (%)
03837
32.5%
13988
33.8%
23964
33.6%
ValueCountFrequency (%)
23964
33.6%
13988
33.8%
03837
32.5%

workload_type
Unsupported

Missing  Rejected  Unsupported 

Missing11789
Missing (%)100.0%
Memory size92.2 KiB

queue_depth
Real number (ℝ)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.06183731
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:35.232968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q316
95-th percentile32
Maximum32
Range31
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.00501135
Coefficient of variation (CV)0.9123826723
Kurtosis-0.6392986085
Mean12.06183731
Median Absolute Deviation (MAD)7
Skewness0.8736586576
Sum142197
Variance121.1102749
MonotonicityNot monotonic
2025-12-15T20:55:35.426937image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
82412
20.5%
42408
20.4%
162354
20.0%
12325
19.7%
322290
19.4%
ValueCountFrequency (%)
12325
19.7%
42408
20.4%
82412
20.5%
162354
20.0%
322290
19.4%
ValueCountFrequency (%)
322290
19.4%
162354
20.0%
82412
20.5%
42408
20.4%
12325
19.7%

workload_block_size_kb
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.187166002
Minimum0.5
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:35.596235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q14
median8
Q316
95-th percentile16
Maximum16
Range15.5
Interquartile range (IQR)12

Descriptive statistics

Standard deviation5.751880885
Coefficient of variation (CV)0.8002988776
Kurtosis-1.147755402
Mean7.187166002
Median Absolute Deviation (MAD)4
Skewness0.476547767
Sum84729.5
Variance33.08413371
MonotonicityNot monotonic
2025-12-15T20:55:35.796712image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
83048
25.9%
162956
25.1%
42902
24.6%
0.52883
24.5%
ValueCountFrequency (%)
0.52883
24.5%
42902
24.6%
83048
25.9%
162956
25.1%
ValueCountFrequency (%)
162956
25.1%
83048
25.9%
42902
24.6%
0.52883
24.5%
Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:36.012810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length10
Median length7
Mean length5.997964204
Min length3

Characters and Unicode

Total characters70710
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDell
2nd rowFujitsu
3rd rowDell
4th rowDell
5th rowSuperMicro
ValueCountFrequency (%)
lenovo2040
17.3%
hpe1956
16.6%
dell1953
16.6%
supermicro1951
16.5%
fujitsu1946
16.5%
inspur1943
16.5%
2025-12-15T20:55:36.467141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u7786
 
11.0%
o6031
 
8.5%
e5944
 
8.4%
r5845
 
8.3%
n3983
 
5.6%
l3906
 
5.5%
i3897
 
5.5%
p3894
 
5.5%
s3889
 
5.5%
L2040
 
2.9%
Other values (12)23495
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)70710
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u7786
 
11.0%
o6031
 
8.5%
e5944
 
8.4%
r5845
 
8.3%
n3983
 
5.6%
l3906
 
5.5%
i3897
 
5.5%
p3894
 
5.5%
s3889
 
5.5%
L2040
 
2.9%
Other values (12)23495
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)70710
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u7786
 
11.0%
o6031
 
8.5%
e5944
 
8.4%
r5845
 
8.3%
n3983
 
5.6%
l3906
 
5.5%
i3897
 
5.5%
p3894
 
5.5%
s3889
 
5.5%
L2040
 
2.9%
Other values (12)23495
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)70710
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u7786
 
11.0%
o6031
 
8.5%
e5944
 
8.4%
r5845
 
8.3%
n3983
 
5.6%
l3906
 
5.5%
i3897
 
5.5%
p3894
 
5.5%
s3889
 
5.5%
L2040
 
2.9%
Other values (12)23495
33.2%
Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:36.714480image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length28
Median length24
Mean length17.75977606
Min length11

Characters and Unicode

Total characters209370
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDell ThinkSystem SR650
2nd rowFujitsu UCS C240
3rd rowDell ThinkSystem SR650
4th rowDell ThinkSystem SR650
5th rowSuperMicro ProLiant DL380
ValueCountFrequency (%)
thinksystem3857
12.3%
sr6503857
12.3%
ucs2937
9.4%
c2402937
9.4%
lenovo2040
 
6.5%
r7402040
 
6.5%
hpe1956
 
6.2%
nf5280m61954
 
6.2%
dell1953
 
6.2%
supermicro1951
 
6.2%
Other values (4)5891
18.8%
2025-12-15T20:55:37.168809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19584
 
9.4%
S12602
 
6.0%
011789
 
5.6%
e9801
 
4.7%
n8841
 
4.2%
i8755
 
4.2%
u7786
 
3.7%
s7746
 
3.7%
o7032
 
3.4%
r6846
 
3.3%
Other values (31)108588
51.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)209370
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19584
 
9.4%
S12602
 
6.0%
011789
 
5.6%
e9801
 
4.7%
n8841
 
4.2%
i8755
 
4.2%
u7786
 
3.7%
s7746
 
3.7%
o7032
 
3.4%
r6846
 
3.3%
Other values (31)108588
51.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)209370
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19584
 
9.4%
S12602
 
6.0%
011789
 
5.6%
e9801
 
4.7%
n8841
 
4.2%
i8755
 
4.2%
u7786
 
3.7%
s7746
 
3.7%
o7032
 
3.4%
r6846
 
3.3%
Other values (31)108588
51.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)209370
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19584
 
9.4%
S12602
 
6.0%
011789
 
5.6%
e9801
 
4.7%
n8841
 
4.2%
i8755
 
4.2%
u7786
 
3.7%
s7746
 
3.7%
o7032
 
3.4%
r6846
 
3.3%
Other values (31)108588
51.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:37.384912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.999066927
Min length3

Characters and Unicode

Total characters47145
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAMD
2nd rowIntel
3rd rowAMD
4th rowAMD
5th rowIntel
ValueCountFrequency (%)
amd5900
50.0%
intel5889
50.0%
2025-12-15T20:55:37.817115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A5900
12.5%
M5900
12.5%
D5900
12.5%
I5889
12.5%
n5889
12.5%
t5889
12.5%
e5889
12.5%
l5889
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)47145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A5900
12.5%
M5900
12.5%
D5900
12.5%
I5889
12.5%
n5889
12.5%
t5889
12.5%
e5889
12.5%
l5889
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)47145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A5900
12.5%
M5900
12.5%
D5900
12.5%
I5889
12.5%
n5889
12.5%
t5889
12.5%
e5889
12.5%
l5889
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)47145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A5900
12.5%
M5900
12.5%
D5900
12.5%
I5889
12.5%
n5889
12.5%
t5889
12.5%
e5889
12.5%
l5889
12.5%
Distinct6967
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:38.142065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters141468
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3667 ?
Unique (%)31.1%

Sample

1st rowABCD9186EFGH
2nd rowABCD6097EFGH
3rd rowABCD3422EFGH
4th rowABCD9676EFGH
5th rowABCD6081EFGH
ValueCountFrequency (%)
abcd3175efgh7
 
0.1%
abcd0186efgh6
 
0.1%
abcd2060efgh6
 
0.1%
abcd1628efgh6
 
0.1%
abcd9374efgh6
 
0.1%
abcd0054efgh6
 
0.1%
abcd0605efgh6
 
0.1%
abcd2991efgh6
 
0.1%
abcd8114efgh6
 
0.1%
abcd2870efgh6
 
0.1%
Other values (6957)11728
99.5%
2025-12-15T20:55:38.694299image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A11789
 
8.3%
E11789
 
8.3%
H11789
 
8.3%
G11789
 
8.3%
B11789
 
8.3%
F11789
 
8.3%
D11789
 
8.3%
C11789
 
8.3%
64808
 
3.4%
54780
 
3.4%
Other values (8)37568
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)141468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A11789
 
8.3%
E11789
 
8.3%
H11789
 
8.3%
G11789
 
8.3%
B11789
 
8.3%
F11789
 
8.3%
D11789
 
8.3%
C11789
 
8.3%
64808
 
3.4%
54780
 
3.4%
Other values (8)37568
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)141468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A11789
 
8.3%
E11789
 
8.3%
H11789
 
8.3%
G11789
 
8.3%
B11789
 
8.3%
F11789
 
8.3%
D11789
 
8.3%
C11789
 
8.3%
64808
 
3.4%
54780
 
3.4%
Other values (8)37568
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)141468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A11789
 
8.3%
E11789
 
8.3%
H11789
 
8.3%
G11789
 
8.3%
B11789
 
8.3%
F11789
 
8.3%
D11789
 
8.3%
C11789
 
8.3%
64808
 
3.4%
54780
 
3.4%
Other values (8)37568
26.6%
Distinct1000
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:39.076441image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters106101
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD3V536VAB
2nd rowD3V619VAB
3rd rowD3V145VAB
4th rowD3V875VAB
5th rowD3V836VAB
ValueCountFrequency (%)
d3v053vab23
 
0.2%
d3v526vab23
 
0.2%
d3v022vab22
 
0.2%
d3v850vab22
 
0.2%
d3v601vab22
 
0.2%
d3v090vab22
 
0.2%
d3v540vab22
 
0.2%
d3v786vab22
 
0.2%
d3v545vab22
 
0.2%
d3v909vab21
 
0.2%
Other values (990)11568
98.1%
2025-12-15T20:55:39.658984image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
V23578
22.2%
315334
14.5%
D11789
11.1%
A11789
11.1%
B11789
11.1%
03662
 
3.5%
53589
 
3.4%
23570
 
3.4%
93565
 
3.4%
43508
 
3.3%
Other values (4)13928
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)106101
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V23578
22.2%
315334
14.5%
D11789
11.1%
A11789
11.1%
B11789
11.1%
03662
 
3.5%
53589
 
3.4%
23570
 
3.4%
93565
 
3.4%
43508
 
3.3%
Other values (4)13928
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)106101
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V23578
22.2%
315334
14.5%
D11789
11.1%
A11789
11.1%
B11789
11.1%
03662
 
3.5%
53589
 
3.4%
23570
 
3.4%
93565
 
3.4%
43508
 
3.3%
Other values (4)13928
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)106101
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V23578
22.2%
315334
14.5%
D11789
11.1%
A11789
11.1%
B11789
11.1%
03662
 
3.5%
53589
 
3.4%
23570
 
3.4%
93565
 
3.4%
43508
 
3.3%
Other values (4)13928
13.1%

host_read_cmds_per_power_cycle
Real number (ℝ)

Unique 

Distinct11789
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113524851.2
Minimum10068676.54
Maximum1998381241
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.2 KiB
2025-12-15T20:55:39.900221image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10068676.54
5-th percentile17050766.09
Q131555111.58
median48718680.91
Q395255878.67
95-th percentile429870973.4
Maximum1998381241
Range1988312564
Interquartile range (IQR)63700767.09

Descriptive statistics

Standard deviation210069853.4
Coefficient of variation (CV)1.850430555
Kurtosis30.64539292
Mean113524851.2
Median Absolute Deviation (MAD)22998684.69
Skewness5.006415387
Sum1.338344471 × 1012
Variance4.412934332 × 1016
MonotonicityNot monotonic
2025-12-15T20:55:40.349735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75598771.131
 
< 0.1%
45502731.451
 
< 0.1%
34808156.581
 
< 0.1%
66642259.081
 
< 0.1%
206661829.21
 
< 0.1%
141328173.71
 
< 0.1%
37988719.271
 
< 0.1%
18052763.411
 
< 0.1%
38449131.81
 
< 0.1%
323115589.81
 
< 0.1%
Other values (11779)11779
99.9%
ValueCountFrequency (%)
10068676.541
< 0.1%
101247111
< 0.1%
10174701.361
< 0.1%
10266389.21
< 0.1%
10374308.361
< 0.1%
ValueCountFrequency (%)
19983812411
< 0.1%
19851292121
< 0.1%
19827694041
< 0.1%
19826021751
< 0.1%
19774623141
< 0.1%